Answer-first summary for fast verification
Answer: (storesDF.withColumn("managerFirstName", split(col("managerName"), " ")[0]) .withColumn("managerLastName", split(col("managerName"), " ")[1]))
The correct answer is option A because it correctly uses the `split` function on the `managerName` column to split the names at the space character and then accesses the first and second elements of the resulting array with indices [0] and [1] to create the `managerFirstName` and `managerLastName` columns, respectively. The other options either incorrectly apply the split function, use wrong indices, or have syntax errors.
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Which of the following code blocks correctly splits the managerName column from DataFrame storesDF at the space character into two new columns named managerFirstName and managerLastName?
A sample of DataFrame storesDF is shown below:
storeId open openDate managerName
0 true 1100746394 Vulputate Curabitur
1 true 944572255 Tempor Augue
2 false 925495628 Aliquam Et
3 true 1397353092 Faucibus Orci
4 true 986505057 Sed Fermentum
storeId open openDate managerName
0 true 1100746394 Vulputate Curabitur
1 true 944572255 Tempor Augue
2 false 925495628 Aliquam Et
3 true 1397353092 Faucibus Orci
4 true 986505057 Sed Fermentum
A
(storesDF.withColumn("managerFirstName", split(col("managerName"), " ")[0]) .withColumn("managerLastName", split(col("managerName"), " ")[1]))
B
(storesDF.withColumn("managerFirstName", col("managerName"). split(" ")[1]) .withColumn("managerLastName", col("managerName").split(" ")[2]))
C
(storesDF.withColumn("managerFirstName", split(col("managerName"), " ")[1]) .withColumn("managerLastName", split(col("managerName"), " ")[2]))
D
(storesDF.withColumn("managerFirstName", col("managerName").split(" ")[0]) .withColumn("managerLastName", col("managerName").split(" ")[1]))
E
(storesDF.withColumn("managerFirstName", split("managerName"), " ")[0]) .withColumn("managerLastName", split("managerName"), " ")[1]))